12 research outputs found
Graphene and Beyond: Recent Advances in Two-Dimensional Materials Synthesis, Properties, and Devices
Since the isolation of graphene in 2004, two-dimensional (2D) materials research has rapidly evolved into an entire subdiscipline in the physical sciences with a wide range of emergent applications. The unique 2D structure offers an open canvas to tailor and functionalize 2D materials through layer number, defects, morphology, moir\ue9 pattern, strain, and other control knobs. Through this review, we aim to highlight the most recent discoveries in the following topics: theory-guided synthesis for enhanced control of 2D morphologies, quality, yield, as well as insights toward novel 2D materials; defect engineering to control and understand the role of various defects, including in situ and ex situ methods; and properties and applications that are related to moir\ue9 engineering, strain engineering, and artificial intelligence. Finally, we also provide our perspective on the challenges and opportunities in this fascinating field
A Biomimetic 2D Transistor for Audiomorphic Computing
This 10-page article, published by Nature Publishing Group, discusses research related to neuromorphic computing. This research involves "... a biomimetic audiomorphic device that captures the neurobiological architecture and computational map inside the auditory cortex of barn owl..." Mimicking barn owls "super sensory neural architectures and associated algorithms through innovative solid-state devices and circuits might provide an alternative route towards energy efficient neuromorphic computing."This article includes the following sections: Abstract; Introduction; Results - Neural map and compute algorithm in barn owl, Artificial coincident detector neuron, Construction of neural auditory computational map, Artificial time delay neurons, and Neuroplasticity; Discussion; Methods; and more.Â
An All-in-One Bioinspired Neural Network
In
spite of recent advancements in artificial neural
networks (ANNs),
the energy efficiency, multifunctionality, adaptability, and integrated
nature of biological neural networks remain largely unimitated by
hardware neuromorphic computing systems. Here, we exploit optoelectronic,
computing, and programmable memory devices based on emerging two-dimensional
(2D) layered materials such as MoS2 to demonstrate a monolithically
integrated, multipixel, and “all-in-one” bioinspired
neural network (BNN) capable of sensing, encoding, learning, forgetting,
and inferring at minuscule energy expenditure. We also demonstrate
learning adaptability and simulate learning challenges under specific
synaptic conditions to mimic biological learning. Our findings highlight
the potential of in-memory computing and sensing based on emerging
2D materials, devices, and integrated circuits to not only overcome
the bottleneck of von Neumann computing in conventional CMOS designs
but also to aid in eliminating the peripheral components necessary
for competing technologies such as memristors
An All-in-One Bioinspired Neural Network
In
spite of recent advancements in artificial neural
networks (ANNs),
the energy efficiency, multifunctionality, adaptability, and integrated
nature of biological neural networks remain largely unimitated by
hardware neuromorphic computing systems. Here, we exploit optoelectronic,
computing, and programmable memory devices based on emerging two-dimensional
(2D) layered materials such as MoS2 to demonstrate a monolithically
integrated, multipixel, and “all-in-one” bioinspired
neural network (BNN) capable of sensing, encoding, learning, forgetting,
and inferring at minuscule energy expenditure. We also demonstrate
learning adaptability and simulate learning challenges under specific
synaptic conditions to mimic biological learning. Our findings highlight
the potential of in-memory computing and sensing based on emerging
2D materials, devices, and integrated circuits to not only overcome
the bottleneck of von Neumann computing in conventional CMOS designs
but also to aid in eliminating the peripheral components necessary
for competing technologies such as memristors
An All-in-One Bioinspired Neural Network
In
spite of recent advancements in artificial neural
networks (ANNs),
the energy efficiency, multifunctionality, adaptability, and integrated
nature of biological neural networks remain largely unimitated by
hardware neuromorphic computing systems. Here, we exploit optoelectronic,
computing, and programmable memory devices based on emerging two-dimensional
(2D) layered materials such as MoS2 to demonstrate a monolithically
integrated, multipixel, and “all-in-one” bioinspired
neural network (BNN) capable of sensing, encoding, learning, forgetting,
and inferring at minuscule energy expenditure. We also demonstrate
learning adaptability and simulate learning challenges under specific
synaptic conditions to mimic biological learning. Our findings highlight
the potential of in-memory computing and sensing based on emerging
2D materials, devices, and integrated circuits to not only overcome
the bottleneck of von Neumann computing in conventional CMOS designs
but also to aid in eliminating the peripheral components necessary
for competing technologies such as memristors
An All-in-One Bioinspired Neural Network
In
spite of recent advancements in artificial neural
networks (ANNs),
the energy efficiency, multifunctionality, adaptability, and integrated
nature of biological neural networks remain largely unimitated by
hardware neuromorphic computing systems. Here, we exploit optoelectronic,
computing, and programmable memory devices based on emerging two-dimensional
(2D) layered materials such as MoS2 to demonstrate a monolithically
integrated, multipixel, and “all-in-one” bioinspired
neural network (BNN) capable of sensing, encoding, learning, forgetting,
and inferring at minuscule energy expenditure. We also demonstrate
learning adaptability and simulate learning challenges under specific
synaptic conditions to mimic biological learning. Our findings highlight
the potential of in-memory computing and sensing based on emerging
2D materials, devices, and integrated circuits to not only overcome
the bottleneck of von Neumann computing in conventional CMOS designs
but also to aid in eliminating the peripheral components necessary
for competing technologies such as memristors
An All-in-One Bioinspired Neural Network
In
spite of recent advancements in artificial neural
networks (ANNs),
the energy efficiency, multifunctionality, adaptability, and integrated
nature of biological neural networks remain largely unimitated by
hardware neuromorphic computing systems. Here, we exploit optoelectronic,
computing, and programmable memory devices based on emerging two-dimensional
(2D) layered materials such as MoS2 to demonstrate a monolithically
integrated, multipixel, and “all-in-one” bioinspired
neural network (BNN) capable of sensing, encoding, learning, forgetting,
and inferring at minuscule energy expenditure. We also demonstrate
learning adaptability and simulate learning challenges under specific
synaptic conditions to mimic biological learning. Our findings highlight
the potential of in-memory computing and sensing based on emerging
2D materials, devices, and integrated circuits to not only overcome
the bottleneck of von Neumann computing in conventional CMOS designs
but also to aid in eliminating the peripheral components necessary
for competing technologies such as memristors
An All-in-One Bioinspired Neural Network
In
spite of recent advancements in artificial neural
networks (ANNs),
the energy efficiency, multifunctionality, adaptability, and integrated
nature of biological neural networks remain largely unimitated by
hardware neuromorphic computing systems. Here, we exploit optoelectronic,
computing, and programmable memory devices based on emerging two-dimensional
(2D) layered materials such as MoS2 to demonstrate a monolithically
integrated, multipixel, and “all-in-one” bioinspired
neural network (BNN) capable of sensing, encoding, learning, forgetting,
and inferring at minuscule energy expenditure. We also demonstrate
learning adaptability and simulate learning challenges under specific
synaptic conditions to mimic biological learning. Our findings highlight
the potential of in-memory computing and sensing based on emerging
2D materials, devices, and integrated circuits to not only overcome
the bottleneck of von Neumann computing in conventional CMOS designs
but also to aid in eliminating the peripheral components necessary
for competing technologies such as memristors
An All-in-One Bioinspired Neural Network
In
spite of recent advancements in artificial neural
networks (ANNs),
the energy efficiency, multifunctionality, adaptability, and integrated
nature of biological neural networks remain largely unimitated by
hardware neuromorphic computing systems. Here, we exploit optoelectronic,
computing, and programmable memory devices based on emerging two-dimensional
(2D) layered materials such as MoS2 to demonstrate a monolithically
integrated, multipixel, and “all-in-one” bioinspired
neural network (BNN) capable of sensing, encoding, learning, forgetting,
and inferring at minuscule energy expenditure. We also demonstrate
learning adaptability and simulate learning challenges under specific
synaptic conditions to mimic biological learning. Our findings highlight
the potential of in-memory computing and sensing based on emerging
2D materials, devices, and integrated circuits to not only overcome
the bottleneck of von Neumann computing in conventional CMOS designs
but also to aid in eliminating the peripheral components necessary
for competing technologies such as memristors
Stochastic resonance in MoS2 photodetector
Here, the authors take advantage of stochastic resonance in a photodetector based on monolayer MoS2 for measuring otherwise undetectable, ultra-low-intensity, subthreshold optical signals from a distant light emitting diode in the presence of a finite and optimum amount of white Gaussian noise